生物地理学优化算法（BBO)解读(含源码） |
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生物地理学优化算法（BBO)解读(含源码）
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目录 一、算法原理： 1.迁移： 2.变异 二、算法流程： 三、matlab代码解读： 三、算法改进思路： 四、算法应用： 四、算法应用： 参考文献 生物地理学优化算法（Biogeography-based optimization）是Dan Simon 教授在2008年提出来的。生物地理学优化算法是受生物地理学原理启发，可以理解为大自然通过物种在地理区域间迁移和漂流，最终达到一种平衡态；这里的物种信息就是优化问题的决策变量。与其他基于种群的优化算法一样，BBO算法也是通过对物种信息的迁移和变异操作的迭代，每一代筛选最适应度值最有的个体作为全局最优值，通过种群的迭代和评估，最终得到最优个体。 原始论文下链链接: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4475427 一、算法原理：BBO算法的基本思想来源于生物地理学理论。如图1所示，生物物种生活在多个栖息地（Habitat）上，每个栖息地用栖息适宜指数（Habitat Suitability Index，HSI）表示，与HSI相关的因素有降雨量、植被多样性、地貌特征、土地面积、温度和湿度等，将其称为适宜指数变量（Suitability Index Variables，SIV） 生物地理学优化算法的解的更新机制主要依赖两种操作：迁移和变异。其原理就是受生物地理学启发得到的。 1.迁移：首先是物种迁移，物种的迁移是有物理模型的，当然这种模型也是统计模型，但是道理是一样的。比如线性模型、余弦模型、二次模型、指数模型。 2.变异为了增加算法的多样性，引入了变异操作；变异操作就和遗传算法中的一样，但是要根据公式来的。 二、算法流程：BBO算法的具体流程为： step1. 初始化BBO算法参数，包括栖息地数量N 、迁入率最大值I 和迁出率最大值E、最大突变率 等参数。 step2. 初始化栖息地，也就是解的初始化，对每个栖息地及物种进行随机或者启发式初始化。 step3. 计算每个栖息地的适宜指数HSI（也就是目标函数的适应度值）； step4.判断是否达到迭代停止条件，如果满足就停止迭代，输出最优解，否则转step5。 step5. 对所有的栖息地执行迁移操作，对每个栖息地计算其迁入率和迁出率，对SIV进行修改，重新计算适宜指数HSI。 step6. 对所有栖息地执行突变操作，根据突变算子更新栖息地物种，重新计算适宜指数HSI。 step7. 转到步骤3进行下一次迭代。 三、matlab代码解读：算法参数初始化： function [OPTIONS, MinCost, AvgCost, InitFunction, CostFunction, FeasibleFunction, ... MaxParValue, MinParValue, Population] = Init(DisplayFlag, ProblemFunction, RandSeed) % Initialize population-based optimization software. % WARNING: some of the optimization routines will not work if population size is odd. OPTIONS.popsize = 50; % total population size OPTIONS.Maxgen = 50; % generation count limit OPTIONS.numVar = 20; % number of genes in each population member OPTIONS.pmutate = 0; % mutation probability if ~exist('RandSeed', 'var') RandSeed = round(sum(100*clock)); end rand('state', RandSeed); % initialize random number generator if DisplayFlag disp(['random # seed = ', num2str(RandSeed)]); end % Get the addresses of the initialization, cost, and feasibility functions. [InitFunction, CostFunction, FeasibleFunction] = ProblemFunction(); % Initialize the population. [MaxParValue, MinParValue, Population, OPTIONS] = InitFunction(OPTIONS); % Make sure the population does not have duplicates. Population = ClearDups(Population, MaxParValue, MinParValue); % Compute cost of each individual Population = CostFunction(OPTIONS, Population); % Sort the population from most fit to least fit Population = PopSort(Population); % Compute the average cost AverageCost = ComputeAveCost(Population); % Display info to screen MinCost = [Population(1).cost]; AvgCost = [AverageCost]; if DisplayFlag disp(['The best and mean of Generation # 0 are ', num2str(MinCost(end)), ' and ', num2str(AvgCost(end))]); end return;GetSpeciesCounts函数： %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [Population] = GetSpeciesCounts(Population, P) % Map cost values to species counts. % This loop assumes the population is already sorted from most fit to least fit. for i = 1 : length(Population) if Population(i).cost < inf Population(i).SpeciesCount = P - i; else Population(i).SpeciesCount = 0; end end return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%代码运行结果： random # seed = 210433 The best and mean of Generation # 0 are 6798.1881 and 8425.6765 The best and mean of Generation # 1 are 6191.0463 and 7712.3762 The best and mean of Generation # 2 are 5975.7976 and 7145.1543 The best and mean of Generation # 3 are 5548.1582 and 6854.2149 The best and mean of Generation # 4 are 5402.7062 and 6284.0483 The best and mean of Generation # 5 are 4136.2399 and 5806.3381 The best and mean of Generation # 6 are 3802.5443 and 5336.0506 The best and mean of Generation # 7 are 3802.5443 and 4818.2664 The best and mean of Generation # 8 are 3424.4632 and 4389.6728 The best and mean of Generation # 9 are 2885.6755 and 3908.8695 The best and mean of Generation # 10 are 2063.7531 and 3485.6253 The best and mean of Generation # 11 are 2063.7531 and 3098.9484 The best and mean of Generation # 12 are 1776.1285 and 2711.0083 The best and mean of Generation # 13 are 1776.1285 and 2531.2169 The best and mean of Generation # 14 are 1776.1285 and 2244.1648 The best and mean of Generation # 15 are 1498.5574 and 2116.3916 The best and mean of Generation # 16 are 1479.9849 and 1957.3969 The best and mean of Generation # 17 are 1239.8981 and 1806.5861 The best and mean of Generation # 18 are 1239.8981 and 1630.9923 The best and mean of Generation # 19 are 1199.4821 and 1505.7131 The best and mean of Generation # 20 are 1173.6721 and 1426.8884 The best and mean of Generation # 21 are 1173.6721 and 1395.8198 The best and mean of Generation # 22 are 1173.6721 and 1349.173 The best and mean of Generation # 23 are 1173.6721 and 1298.9352 The best and mean of Generation # 24 are 1173.6721 and 1366.31 The best and mean of Generation # 25 are 1173.6721 and 1448.2162 The best and mean of Generation # 26 are 1171.9469 and 1332.6789 The best and mean of Generation # 27 are 1171.9469 and 1401.491 The best and mean of Generation # 28 are 1119.6099 and 1293.9546 The best and mean of Generation # 29 are 1038.4718 and 1347.819 The best and mean of Generation # 30 are 1036.7466 and 1347.0559 The best and mean of Generation # 31 are 982.6845 and 1329.0154 The best and mean of Generation # 32 are 976.182 and 1279.9247 The best and mean of Generation # 33 are 976.182 and 1311.9903 The best and mean of Generation # 34 are 976.182 and 1274.8326 The best and mean of Generation # 35 are 974.4569 and 1225.205 The best and mean of Generation # 36 are 974.4569 and 1179.6202 The best and mean of Generation # 37 are 974.4569 and 1135.61 The best and mean of Generation # 38 are 974.4569 and 1149.5376 The best and mean of Generation # 39 are 974.4569 and 1152.9323 The best and mean of Generation # 40 are 974.4569 and 1219.1953 The best and mean of Generation # 41 are 974.4569 and 1176.0811 The best and mean of Generation # 42 are 974.4569 and 1234.05 The best and mean of Generation # 43 are 897.0742 and 1176.734 The best and mean of Generation # 44 are 897.0742 and 1169.1137 The best and mean of Generation # 45 are 897.0742 and 1188.9399 The best and mean of Generation # 46 are 895.349 and 1160.2358 The best and mean of Generation # 47 are 895.349 and 1149.5594 The best and mean of Generation # 48 are 894.3968 and 1224.6265 The best and mean of Generation # 49 are 815.3731 and 1099.1538 The best and mean of Generation # 50 are 815.3731 and 1084.9362 0 duplicates in final population. 50 legal individuals in final population. Best chromosome = 637 697 722 737 755 897 907 910 915 926 927 967 1033 1042 1063 1066 1098 1127 1238 7845 Diversity measure = 7694 MinCost = 1.0e+03 * 列 1 至 15 6.7982 6.1910 5.9758 5.5482 5.4027 4.1362 3.8025 3.8025 3.4245 2.8857 2.0638 2.0638 1.7761 1.7761 1.7761 列 16 至 30 1.4986 1.4800 1.2399 1.2399 1.1995 1.1737 1.1737 1.1737 1.1737 1.1737 1.1737 1.1719 1.1719 1.1196 1.0385 列 31 至 45 1.0367 0.9827 0.9762 0.9762 0.9762 0.9745 0.9745 0.9745 0.9745 0.9745 0.9745 0.9745 0.9745 0.8971 0.8971 列 46 至 51 0.8971 0.8953 0.8953 0.8944 0.8154 0.8154 Hamming = 7694迭代收敛曲线：
完成代码可以私信获取。 三、算法改进思路：1.与其他算法（如遗传算法、差分进化、灰狼等算法）的结合； 2.引入其他变异、交叉算子； 3.改进迁移和变异的公式； 四、算法应用：BBO算法具有诸多优点，提出以来被应用于诸多应用，除了基本的测试函数，参数辨识应用外，主要有： 1.基于BBO算法图像分割中的应用； 2.基于BBO算法的PID参数整定中的应用； 3.基于BBO算法的交通规划中的应用研究； 4.基于BBO算法的动态车间调度中的应用研究； 5.基于BBO算法神经网络（BP）、SVM参数（结构）优化 6.基于BBO算法的无线传感器网WSN覆盖问题 7.基于BBO算法的医院管理资源调度研究（代码） 四、算法应用：基于生物地理信息的多目标进化算法 源码获取：🍞正在为您运送作品详情 参考文献[1] Simon D.Biogeography-based optimization[J].IEEE Trans-actions on Evolutionary Computation，2008（6）：702-713. [2]徐志丹, 莫宏伟. 基于生物地理信息的多目标进化算法[J]. 2010. |
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